train-pose

name	train-pose
description	Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted.

train-pose

Build a CSI→pose model without overstating it. The project has a retracted 92.9%/100% history — the discipline below exists so it never recurs.

The non-negotiable: mean-pose baseline first

A pose model that always predicts the dataset's mean pose already scores ~50% PCK. Quote PCK only as a delta over that baseline, on a held-out split with no subject or temporal leakage. Example honest result (ADR-181):

Held-out PCK@20 59.5% vs a 50% mean-pose baseline = +9.4 pp real signal — MEASURED.

Paths

• camera-supervised (ADR-079) — MediaPipe Pose labels the camera frame; paired CSI trains the net. Train/infer in one camera frame so the skeleton aligns.
• camera-free (WiFlow, ADR-152) — no camera at inference; geometry-conditioned.
• in-browser (ADR-181) — WebGPU/WASM trainer; the active backend is shown as a badge (honest about what's executing).

Before you publish a number

1. Run the mean-pose baseline on the same split.
2. Report (model − baseline) in pp, with the split definition (chronological / blocked-gap / grouped-bucket; no leakage).
3. ruview.claim_check the writeup — it flags any untagged or 100%/perfect claim.
4. If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.